Paper

Members of the QUEST team have published a new approach for analysing multivariate proxy records with recurrence networks. The idea is to first consider each proxy separately as a recurrence network, then combine them as layers of a multiplex network, and derive a similarity measure from this resulting multiplex recurrence network. This approach can be used to reveal periods of converging dynamics or very disperse variability, as demonstrated on an example of palaeo vegetation development during the last 16 ka in East Asia.

The “inner composition alignment approach” (IOTA) has been suggested in the past for detecting interdependencies between short time series. QUEST members have now published a new study with a modification of this approach (called mIOTA). The new extension overcomes the drawbacks that IOTA is unable to distinguish between positive and negative correlations, and that the null distribution for IOTA is biased towards higher values. Although the new method cannot detect the direction of the interdependencies (unlike IOTA), it outperforms standard tools for detecting interdependencies (Pearson correlation, Spearman correlation, Kendall’s τ). The method is used to derive econo-climatic networks of interdependencies between economic indicators and climatic variability for Sub-Saharan Africa and South Asia including India.

In a new publication we propose a novel approach for analysing data with uncertainties, which is typical, e.g., in palaeoclimate observations. A time series of single values is replaced by a time series of probabilities; the binary matrix representation of recurrences is then replaced by a matrix of probability values, which represent that a recurrence at a certain time appears. This approach is the base for a novel transition test, applying concepts from recurrence and complex network analysis. We demonstrate the potentials on examples from present-day climate (ENSO), palaeoclimate (Indian summer monsoon), and stock markets.

Time series of probability distributions of proxy values, allowing for uncertainty analysis.

The first publication by members of the QUEST team (Norbert & Seb) has recently been published in Nature Communications on a giant see-saw in the monsoonal realm. Find the paper here: http://www.nature.com/articles/ncomms12929